Global clear sky near-surface imagery from multiple satellite daily imagery time series

Abstract We develop a new statistically-robust adaptive regression method (SARM) to extract clear sky true color imagery, approximating the near-surface imagery, derived from multiple satellite daily imagery time series, while avoiding artifacts due to clouds and cloud shadows. We compare the SARM-derived near-surface imagery against simpler approaches for various surface types, and perform a quantitative evaluation. Existing mapped daily imagery from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20, and the Ocean and Land Colour Instrument (OLCI) on the Sentinel-3A and Sentinel-3B satellites is used to produce global clear sky near-surface imagery over various time intervals. We provide several examples of satellite-derived clear sky near-surface imagery over various regions to show potential applications. In addition, we apply this new method to derive clear sky near-surface imagery using higher spatial resolution Landsat-8 data, and discuss characteristics and limitations of our approach. The clear sky near-surface imagery is a useful satellite-derived product, representing the human perception of Earth’s near-surface features, which can be more directly interpreted and easily understood by the general public, and aids visualization and interpretation of various types of satellite derived data.